Fundamentals of Digital Image Correlation

Since DIC is a type of strain sensing, it requires a testing apparatus to gather information about. Like a traditional extensometer, DIC systems can be seen as an accessory for a material test; all the other testing equipment is still required, including testing machines, force sensors, and computer processing equipment. The exact configuration varies depending on the type of test.

DIC introduces the additional limitation that the surface of the material sample must be visible, but for most types of testing this is easily achieved.

To understand DIC, it is helpful to think in terms of input-process-output. DIC algorithms require certain information as input. The algorithms process the input information in a certain way, which creates the desired information (strain, etc.) as output. The main input required by DIC is a series of images. These images must be taken sequentially, and they must be taken from the same location relative to the sample. Therefore, DIC requires at least one static camera which can record images of the surface of interest in a defined measurement sequence.

Though DIC can technically be performed with only a single camera, additional pieces of equipment are often used to make obtaining results easier and more accurate. Camera accessories are one example of this. Most DIC applications require the use of camera mounting equipment and additional lighting. Various types of lenses may be used to achieve the best field of view and distortion properties; these particulars are beyond the scope of this article.

Another common DIC practice is to use two cameras instead of one. DIC with one camera is called 2D-DIC, while DIC with two cameras is known as 3D-DIC (also called stereo-DIC). With 3D-DIC, paired cameras record images of the same surface. We can then compare image pairs taken at the same time to obtain more information than what can be gleaned from a single camera. 2D-DIC and 3D-DIC each have their advantages. 2D-DIC is less costly, easier to set up, and easier to use, but requires the camera to be perpendicular to the deforming surface to give accurate results. 3D-DIC is sometimes more accurate but is more expensive and often requires lengthy calibration procedures. One major advantage of 3D-DIC is its ability to measure changes in depth. While a single camera is unable to detect the distance from the camera to any point of interest, 3D-DIC provides depth perception by comparing two images captured from different positions.

One more consideration is crucial: it is important to ensure that the material sample surface has distinctive features that can be identified and tracked in multiple images. A flat, uniformly colored surface would provide few identifiable features. To prevent this, testing engineers apply a black and white speckle pattern (also called a stochastic pattern) to material samples. This can be achieved quickly and easily by spray-painting the material surface to a solid white, then applying a top layer of black paint with a thin mist. The resulting speckle is high-contrast and non-uniform, making it easy to detect and identify any point on the surface by tracking a distinctive region of the paint pattern.

Once a series of images has been taken, the first step of DIC is identify key points. To do this, DIC software defines a “mesh,” which is a set of arbitrarily defined points on the material surface in the image. Convention and convenience dictate that meshes are regular, which means that the chosen points are evenly spaced.

Once these mesh points are chosen for one image, we need a means to recognize the same points in other images (in other words, to track them). To do this, we take a small rectangular region around each point. These regions are called subsets. Each mesh point has its own subset, with the mesh point at the center.

Because the sample was painted with a speckle pattern, the appearance of each subset is highly unique. Thus, we can identify the location of each subset in each image in the series, and from it we can estimate the location of the corresponding mesh point.

This process is not as straightforward as it might appear because the material surface (and thus the paint pattern we use to identify the subset) is deforming during a test. This causes the subset to appear stretched in later images, which makes it more difficult to identify.

The heart of DIC algorithms is a mathematical construct called the correlation criterion (CC). The CC is a formula that looks at the difference in appearance between two subsets and quantifies it with a single number. Comparing two identical subsets gives a CC of zero, while comparing very different subsets gives a large CC.

DIC algorithms track mesh points by taking the original subset and looking for the corresponding subset in subsequent images. The algorithm essentially uses a guess-and-check method, taking a subset in the current image (the guess) and comparing it with the original using the correlation criterion (the check). It selects the new subset with the lowest correlation criterion. This method can identify corresponding subsets even when the material is greatly deformed.

Once the DIC algorithm has calculated the real-world displacement of a set of points on the material surface, determining the strain is straightforward. As with any other application, the strain between two points on the material surface is equal to the change in distance between them divided by the original distance. Thus, for any pair of mesh points, DIC can determine the strain between them at any point in the test.

So far, we have been talking about DIC using discrete points (the mesh). However, DIC is not limited to measuring deformation/strain only at these points! In fact, DIC can estimate the deformation at any point on the material surface, even if it falls between mesh points. This is done by a process called interpolation, which takes the discrete mesh of displacement values and estimates the displacement everywhere between them. This creates a continuous deformation map of the entire surface, allowing us to calculate strain between any two points on the sample.